Semi-automatic Bone Marrow Evaluation in PETCT for Multiple Myeloma

Leydon, Patrick; O’Connell, Martin; Greene, Derek; Curran, Kathleen M.

doi:10.1007/978-3-319-60964-5_30

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Although our original datasets were PET-CT, we utilised only the CT component in the research presented. The methods we have outlined can be applied with little modification to the PET component of the scan, and improve accuracy of previous bone metabolism assessment tools which are reliant on artifact prone thresholding approaches [63,64].…”

Section: Discussionmentioning

confidence: 99%

Bone segmentation in contrast enhanced whole-body computed tomography

Leydon¹,

O’Connell²,

Greene³

et al. 2022

Biomed. Phys. Eng. Express

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Segmentation of bone regions allows for enhanced diagnostics, disease characterisation and treatment monitoring in CT imaging. In contrast enhanced whole-body scans accurate automatic segmentation is particularly difficult as low dose whole body protocols reduce image quality and make contrast enhanced regions more difficult to separate when relying on differences in pixel intensities. This paper outlines a U-net architecture with novel preprocessing techniques, based on the windowing of training data and the modification of sigmoid activation threshold selection to successfully segment bone-bone marrow regions from low dose contrast enhanced whole-body CT scans. The proposed method achieved mean Dice coefficients of 0.979 ±0.02, 0.965 ±0.03, and 0.934 ±0.06 on two internal datasets and one external test dataset respectively. We have demonstrated that appropriate preprocessing is important for differentiating between bone and contrast dye, and that excellent results can be achieved with limited data.

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Section: Discussionmentioning

confidence: 99%

Bone segmentation in contrast enhanced whole-body computed tomography

Leydon¹,

O’Connell²,

Greene³

et al. 2022

Biomed. Phys. Eng. Express

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show abstract

Section: Discussionmentioning

confidence: 99%

Bone Segmentation in Contrast Enhanced Whole-Body Computed Tomography

Leydon¹,

O’Connell²,

Greene³

et al. 2020

Preprint

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Segmentation of bone regions allows for enhanced diagnostics, disease characterisation and treatment monitoring in CT imaging. In contrast enhanced wholebody scans accurate automatic segmentation is particularly difficult as low dose whole body protocols reduce image quality and make contrast enhanced regions more difficult to separate when relying on differences in pixel intensities.This paper outlines a U-net architecture with novel preprocessing techniques, based on the windowing of training data and the modification of sigmoid activation threshold selection to successfully segment bone-bone marrow regions from low dose contrast enhanced whole-body CT scans. The proposed method achieved mean Dice coefficients of 0.979 ±0.02, 0.965 ±0.03, and 0.934 ±0.06 on two internal datasets and one external test dataset respectively. We have demonstrated that appropriate preprocessing is important for differentiating between bone and contrast dye, and that excellent results can be achieved with limited data.

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“…The study of [ 36 ] focuses on detecting lesions in femur cortical bones, in which a probabilistic, spatially dependent density model has been developed to automatically identify bone marrow infiltration in low-dose CT. In [ 37 ] a semiautomatic software was developed to perform pixel thresholding based segmentation for the assessment of bone marrow metabolism while automatic quantification of bone marrow myeloma volume was conducted in [ 38 ]. A hybrid iterative reconstruction technique was used to compare the diagnostic performance of conventional radiography (CR) and whole-body low-dose computed tomography (WBLDCT) with a comparable radiation dose reconstructed for MM staging [ 39 ].…”

Section: Introductionmentioning

confidence: 99%

Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on ⁶⁸Ga-Pentixafor PET/CT Imaging Using Deep Learning Methods

Tetteh

Lipková

et al. 2018

Contrast Media & Molecular Imaging

119

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The identification of bone lesions is crucial in the diagnostic assessment of multiple myeloma (MM). 68Ga-Pentixafor PET/CT can capture the abnormal molecular expression of CXCR-4 in addition to anatomical changes. However, whole-body detection of dozens of lesions on hybrid imaging is tedious and error prone. It is even more difficult to identify lesions with a large heterogeneity. This study employed deep learning methods to automatically combine characteristics of PET and CT for whole-body MM bone lesion detection in a 3D manner. Two convolutional neural networks (CNNs), V-Net and W-Net, were adopted to segment and detect the lesions. The feasibility of deep learning for lesion detection on 68Ga-Pentixafor PET/CT was first verified on digital phantoms generated using realistic PET simulation methods. Then the proposed methods were evaluated on real 68Ga-Pentixafor PET/CT scans of MM patients. The preliminary results showed that deep learning method can leverage multimodal information for spatial feature representation, and W-Net obtained the best result for segmentation and lesion detection. It also outperformed traditional machine learning methods such as random forest classifier (RF), k-Nearest Neighbors (k-NN), and support vector machine (SVM). The proof-of-concept study encourages further development of deep learning approach for MM lesion detection in population study.

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Semi-automatic Bone Marrow Evaluation in PETCT for Multiple Myeloma

Cited by 4 publications

References 17 publications

Bone segmentation in contrast enhanced whole-body computed tomography

Bone segmentation in contrast enhanced whole-body computed tomography

Bone Segmentation in Contrast Enhanced Whole-Body Computed Tomography

Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on ⁶⁸Ga-Pentixafor PET/CT Imaging Using Deep Learning Methods

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Semi-automatic Bone Marrow Evaluation in PETCT for Multiple Myeloma

Cited by 4 publications

References 17 publications

Bone segmentation in contrast enhanced whole-body computed tomography

Bone segmentation in contrast enhanced whole-body computed tomography

Bone Segmentation in Contrast Enhanced Whole-Body Computed Tomography

Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on 68Ga-Pentixafor PET/CT Imaging Using Deep Learning Methods

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Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on ⁶⁸Ga-Pentixafor PET/CT Imaging Using Deep Learning Methods